Teaching

Personal Robot Coach (2014-2018)

This project explores how the embodiment and feedback of social robots, as well as the personalization of a bodyweight workout, affect the user's motivation to do sports.

Introduction

Diseases of affluence, like obesity, are one of the significant challenges in the 21st century. People tend to eat unhealthily and do less sportive activities. Governments try to counter these developments with funded projects. However, those approaches do not achieve success yet. People lack time or enthusiasm to exercise regularly. This opens the focus for new methods to engage and motivate people to work out every day with enthusiasm and goal orientation. Technological advances in the consumer market like smartphone applications or exergaming (Nintendo Wii, Microsoft Kinect) show great potential for changing exercising habits. After all, a limiting factor of current studies is the novelty effect.

Motivation

Therefore, research is needed which systematically investigates the different factors that shape and establish long-term relationships between robots and humans. In this work, we focus on the aspects of feedback, adaption, and preference learning for socially assistive robots (SAR) that are important for long-term interaction. To examine these after novelty effects wear off, we create an application scenario where users are accompanied by a social robot to do a repeated exercising workout. The primary goal for the adaption process is to find the preferred exercises and workout intensity for each participant during the interaction. Thus, this research project will intersect with three main topics in HRI: socially assistance, long-term cooperation and machine learning techniques for adaption and personalization.

Recent Sport Scenarios

Rowing Body Weight Workout

Indoor Cycling

Research Questions

The central research questions of this project are how and if a social robot can boost the user's motivation to exercise in the long-term. Therefore several secondary issues have to be investigated:

RQ2: What effects does the embodiment have on the impression of the robot [2]?

RQ3: Can motivational feedback provided by a robot help users to exercise longer [3]?

RQ4: How does embodiment affect exercising motivation?

RQ5: How can a system adapt to the user's preferences in online interactions[4]?

RQ6: Is adaptation required or is adaptivity sufficient?

Current Project Results

Koehler Effect:

We have investigated whether the Koehler Effect can be replicated with humanoid companions. We found that people experience a motivational gain when exercising with a robotic partner (RC )compared to working out individually (IC) or with a robot instructor (RI) [1].

Feedback:

We found that acknowledging feedback from a robotic instructor (RIF) during bodyweight training enhances the user's motivation to a robot instructor that does not give feedback (RI) [3].

Embodiment:

We found that embodied robot companions (RC, RCF) lead to longer exercising time compared to virtual agents (HHP, NHP).

Long-Term:

During an 18-days long-term isolation study, we compared a robotic instructor versus a computer display for indoor cycling exercising. We found that participants had higher exercise compliance in the robot condition. [5].

Preference Learning and Adaptation in HRI:

We found that the embodiment of the learning agent does not influence the user's perception of the adaptation quality. Furthermore, we showed that a dueling bandit learning approach is suitable for HRI and achieves better preference results than a randomized learning approach [4].

Adaptation

We found that an adaptive robot is perceived as warmer and to be more trustful.